Dynamic Vehicle Routing Problem with Prompt Confirmation of Advance Requests

TL;DR

This paper introduces a new dynamic vehicle routing problem that combines prompt confirmation of advance trip requests with continual route optimization, using reinforcement learning to maximize served requests in real-time transit services.

Contribution

It formulates a novel problem integrating prompt request confirmation with ongoing route optimization and develops a reinforcement learning-guided computational approach for real-world application.

Findings

Significantly increases the number of served requests.

Provides prompt confirmation alongside continual route optimization.

Demonstrates effectiveness on real-world transit data.

Abstract

Transit agencies that operate on-demand transportation services have to respond to trip requests from passengers in real time, which involves solving dynamic vehicle routing problems with pick-up and drop-off constraints. Based on discussions with public transit agencies, we observe a real-world problem that is not addressed by prior work: when trips are booked in advance (e.g., trip requests arrive a few hours in advance of their requested pick-up times), the agency needs to promptly confirm whether a request can be accepted or not, and ensure that accepted requests are served as promised. State-of-the-art computational approaches either provide prompt confirmation but lack the ability to continually optimize and improve routes for accepted requests, or they provide continual optimization but cannot guarantee serving all accepted requests. To address this gap, we introduce a novel problem formulation of dynamic vehicle routing with prompt confirmation and continual optimization. We propose a novel computational approach for this vehicle routing problem, which integrates a quick insertion search for prompt confirmation with an anytime algorithm for continual optimization. To maximize the number requests served, we train a non-myopic objective function using reinforcement learning, which guides both the insertion and the anytime algorithms towards optimal, non-myopic solutions. We evaluate our computational approach on a real-world microtransit dataset from a public transit agency in the U.S., demonstrating that our proposed approach provides prompt confirmation while significantly increasing the number of requests served compared to existing approaches.